Implementation: CIFAR-10 [1]} and ResNet-50 [2]} are used to test the three strategies. We embed ConAM at the beginning of each block and take the size of the local receptive field as 1/2 of the input spatial size of each block. An NVIDIA 2080TI graphic processing unit (GPU) is used to implement the experiment. Data augmentation involves random cropping, random horizontal flipping, and Cutout [3]}. Random cropping fills four zeros on all borders of the image, and then randomly crops the image with a size of 32 \(\times \)  32. We use the SGD optimizer with a momentum of 0.9 and a weight decay of 5e-4. A total of 250 epochs is set for training, with a batch size of 128. The training accuracy is recorded every 30 iterations in each epoch, and the test accuracy is recorded once in each epoch. To clearly understand the performance of these three strategies, two kinds of experiments with different learning rate strategies are designed as shown in TABLE REF  and each experiment is conducted three times.
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